observations, lecture 2 research satellite observations of

Observations, lecture 2Research satellite observations of water vapour

William Lahoz, [email protected]

www.nilu.nowww.nilu.no

William Lahoz, [email protected]

WAVACS Summer School, ”Water vapour in the climate system”Cargese, Corsica, France, 14-26 September 2009

• Features of observations

• Research satellites and water vapour

Outline


• Features of atmospheric water vapour (stratosphere)

• Benefits of research satellites & the future

1. Resolution (temporal & spatial)

2. Frequency (temporal & spatial)

3. Frequency/wavelength of measurement (region of EM spectrum)

4. Radiometric noise (signal/noise ratio)

5. Coverage (global/local)

6. Geometry (nadir/limb)

Features of observations

Representation of the ”truth”


6. Geometry (nadir/limb)

7. Level of data (0: photons; 1: radiances; 2: geophysical parameters)

8. Errors (random, systematic – biases, “representativeness”)

9. Platform (sondes, aircraft, satellites – this has a bearing onresolution)

10. Influences on time/space evolution (dynamics: temperature, winds,ozone; chemistry: ozone, ClO).

Observations are our representation of the “Truth”

Photons (L0 data)

Radiances (L1 data) algorithms

What satelliteinstrument receives


Radiances (L1 data)

Geophysical parameters (L2 data)

algorithms

e.g. profiles (ozone), total column (ozone)

Scientists normally work with L2 data

L2:

� Easier to assimilate than those of L1: historically L2 data hastended to be assimilated before L1

� Recent ideas from Rodgers (“information content”) -> alleviateproblems associated with L2 data (e.g. a priori information)

L1:

Level of data L1/L2:


L1:

� less “contaminated” (e.g. by a priori information)

� Errors are less correlated than for L2 data

� Tendency to assimilate radiances: nadir radiances alreadyassimilated by met agencies; limb radiances are much harder toassimilate

� Improvements in analyses & forecast skill at NWP centres

What does Level 2 (L2) data look like?


Ozone data at 10 hPa (approximately 30km in height) for 1 February 1997 fromthe MLS (Microwave Limb Sounder)instrument onboard the UARS (UpperAtmosphere Research Satellite) satellite.Blue denotes relatively low ozone values;red denotes relatively high ozone values.

Ozone at 10 hPa (about 30 km in height) from theMIPAS (Michelson Interferometer for PassiveAtmospheric Sounding) instrument onboard theEnvisat satellite, at 1200 UTC on 23 September2002.

Note the gaps between the satellite orbits

See data assimilation lecture (observation # 9 lecture)

Real resolution of observations could be coarser than that implied by theapparent resolution/frequency of the observations

-> correlations (horizontal/vertical) between observations

Correlations taken into account in the observation errors covariance(characterizes observation errors)

Resolution of observations


(characterizes observation errors)

-> data assimilation (see later): non-diagonal errors; correlations betweenobservations -> thin to give a reduced density of observations &represent observation field appropriately

In the stratosphere, horizontal correlations tend to be larger than introposphere: flow dominated by smaller wavenumbers in thestratosphere

GOES-8: ~1 kmHurricane Erin

09/09/01 ~1530 Z

Example of spatial resolution


Courtesy James Purdom

Hurricane Erin

09/09/01 ~1530 Z

MODIS: ~250m


Courtesy James Purdom

Relatively good horizontal resolution

Relatively poor vertical resolution

Relatively poor horizontal resolution

Relatively good vertical resolution

Met agencies use

these data mainly


these data mainly

Research groups use

these data mainly

Courtesy NATO ASI 2003

Types of satellite observations: frequencies

List of research satellites (non-exhaustive):

1. Infrared (IR): ISAMS (UARS), MIPAS (Envisat), HIRDLS (Eos-Aura)

EM spectrum & observations


Aura)

2. Visible (Vis): GOME (ERS-2), SCIAMACHY (Envisat)

3. Ultraviolet (UV): GOME (ERS-2), GOMOS & SCIAMACHY (Envisat)

4. Microwave: MLS (UARS), Eos MLS (Eos Aura)

Variety-> opportunity to evaluate observations (e.g. UARS, Envisat)

EM spectrum properties: e.g. microwaves less affected by clouds

• Conventional: surface, sondes (local coverage; high spatial & temporal resolution) – temperature, humidity, winds

• Aircraft: local coverage; high spatial & temporal resolution –temperature, humidity, winds

• Satellites:

Observation types used by the Met Office

Satellites:

• Operational satellites: ATOVS, Satwinds, SSMI (nadir; global coverage; low spatial & temporal resolution) – temperature, humidity, winds

• Research satellites: (nadir & limb) Interest at NWP centres: e.g. SCIAMACHY ozone at ECMWF

Now part of Global Observing System

See lectures by W. Bell

ATOVS Global coverage

©Crown copyright, Met Office 16/10/02:

Aircraft Local coverage

Observations used by Met Office

NOAA-15 NOAA-16 NOAA-17

Polar orbiters

Courtesy ECMWF

Goes-W Goes-W Met-7 Goes-W GMS(Goes-9)

Geostationary satellites

Three ways:

Passive technologies: sense LW radiation emitted by atmosphere,SW reflected by atmosphere.

- imaging (optically thin -> information on Earth surface)

- sounding (optically thick -> information on atmosphere)

Sounding the atmosphere using satellites


Active technologies: emit radiation & measure how muchscattered/reflected back

GPS: measure phase delay of signal as it is refracted inatmosphere

Types of satellite observations: orbits

1. Geostationary (fixed point over the equator): 60N-60S

Only one orbit: 35,800 km; ¼ Earth’s surface

Satellite orbits


2. Polar: quasi-global (e.g. 600 km Hubble, 225-250 km Shuttle )

3. Sun-synchronous (fixed equator crossing time)

4. Non sunsynchronous (variable equator crossing time)


Geostationary satellite orbit

courtesy NASDA Quasi-polar satellite orbits courtesy www.planetearthsci.com

1. Sun-synchronous satellites (e.g. ESA Envisat, NASA Eos Aura):

� Instruments look away from the sun (no manoeuvre toprevent the sun damaging the instruments)

� Cannot observe the diurnal cycle at a particular place (e.g.diurnal cycle of NO, NO2)

Diurnal cycle & orbit


2. Non sunsynchronous satellites (e.g. NASA UARS):

� Can observe the diurnal cycle at a particular place

� Have to do manoeuvres to prevent the sun damaging theinstruments -> North look/South look for UARS MLS

Random: Assumed Gaussian; it is reduced by taking averages

Is it Gaussian? How do we check? What could be non-Gaussian? -> bi-modal distributions, e.g., precipitation

Systematic (bias): Can vary temporally & spatially.

If fixed and known, it should be removed

Representativeness: Occurs when information is represented at a scale

Observation errors


Representativeness: Occurs when information is represented at a scaledifferent from the source of the information (e.g. representation ofsonde data in a GCM grid).

More important for small-scale observations.

X X?

Depends on position & time of observations

Interesting case: ozone -> dynamics dominates in lower stratosphere

(except for ozone hole conditions), chemistry dominates in the upperstratosphere

Influence of chemistry/dynamics on observations


stratosphere

Between these limits, both are important

-> can make it difficult to study the temporal/spatial distribution of ozone

-> need to take account of both dynamics and chemistry

How? -> design of parametrizations, coupled models

For observations from operational satellites, see W. Bell lectures

Examples from NASA, ESA, CSA & JAXA

NASA:

UARS (mainly HALOE & MLS): Science: JAS 1994, Cal-val: JGR 1996

Water vapour observations from research satellites


UARS (mainly HALOE & MLS): Science: JAS 1994, Cal-val: JGR 1996

EOS Aura: (mainly Eos MLS): EOS Aura special issue in IEEE, 2006, Vol.44, special issue on EOS Aura validation in JGR, 2008, Vol. 113

EOS Aura part of the EOS “A-Train”(http://www.spacetoday.org/Satellites/TerraAqua/ATrain.html)

ESA:

Envisat (mainly MIPAS and SCIAMACHY):

Use of data assimilation to evaluate Envisat: See obs 9 lecture

CSA:

SCISAT-1/ACE: incl. ozone, water vapour, methane, N2O and NO2

NASDA-JAXA:


NASDA-JAXA:

ADEOS: ADEOS-TOMS (ozone column), ILAS (temperature,ozone, water vapour & other constituents)

ADEOS-II: water column, precipitation & ocean and iceparameters (AMSR), temperature, ozone & other constituents(ILAS-II)

Picture of the flow

Polar

Features of atmospheric water vapour


Left: Eulerian picture of the atmospheric circulation. Right: Lagrangian pictureof the atmospheric circulation. NP and SP stand for North Pole and South Pole,respectively. Northern Winter conditions are assumed.

Tracer data show Lagrangian picture is appropriate one Hadley cell

Ferrel cell

Polar

cell

Water vapour

tape recorder

Mote et al., JGR, 1996

UARS MLS

HALOE


SAGE II

TEM back

trajectory

Geopotential height: snapshot of the stratosphere

Interhemispheric comparisons

Polar vortex

Aleutian High


Met Office analyses of geopotential height. Left: NH winter, 10 hPa, 11 January 1992 (Lahoz et al.1994, JAS). Right: SH winter, 10 hPa, 9 September 1992 (Lahoz et al. 1996, QJRMS).

Quasi-stationary anticyclone

100 hPa 10 hPa 1 hPa

Quasi-stationary anticyclone SH winter;

31 August 2003

- see later: MIPAS cross-sections

We would like to use cross-sections that cutacross polar vortex & anticyclone

MIPAS, Lahoz et al. 2006, QJRMS

Met Office geopotential height

S

Quasi-stationary anticyclone SH winter;

24 September 2003

Vertical structure of cross-sections:

SH winter 2003 (Lahoz et al. 2006, QJRMS)

Events depicted with Met Office geopotential height: 31/08/03

Ozone H2O

Low amounts of

O3 in polar vortex:

Transport & chemistry

CH4

N2O

Note: low amounts of CH4, N2O; high amounts of H2O in vortex:

Sources/sinks in stratosphere & tracers

Start of orbit, SDescent in

polar vortex

3-D filamentation event: Met Office geopotential height 17/10/03

100 hPa 10 hPa 1 hPa

Potential vorticity, PV: tracer

ECMWF PV maps 850 K

11/10/03 – 26/10/03

High magnitude: high lat air

Low magnitude: low lat air

850 K approx 10 hPa

S

S

Cross-section “1”

17/10/03

Ozone H2O

CH4

N2O

S

Ozone CH4

Cross-section “2”

17/10/03

S

Note ”champagne glass”

Schematic for SH stratospheric mid winter:

Tracer isopleth

e.g. H Oe.g. H2O

Polar vortex

Travelling anticyclone

Understanding spatial/temporal variability of the atmosphere requiresconsidering:

•Meteorology, transport, chemistry & their interaction

Advantageous to use satellite observational geometry (along orbit track)

By considering satellite observational geometry & synergies betweenmeasurements:

Atmosphere:


measurements:

•Meteorology (geopotential height,...)

•Tracer species (CH4, N2O, H2O,...)

•Chemical species (O3,...)

•Derived products (PV,...)

We can build up a consistent picture (spatial/temporal variability) of theatmosphere

& improve understanding (this helps in looking at, e.g., temporal evolution)

� Test Earth Observation concepts

� Today’s research satellite is tomorrow’s operational (NWP)satellite (mainly ozone, but could help with water vapour)

Benefits from research satellites


� Information to help make chemical forecasts

Interest in research satellites by the met agencies make themmore attractive to the EO community

• Satellite data have been v. successfully exploited by new dataassimilation, DA, schemes (4d-var, ECMWF). DA schemes -> introducingadditional satellite data that is well characterized improves system

• Combined availability of new & accurate satellite observations &improvements in models -> improved extraction of information contentfrom these new observations using DA techniques

• Proliferation of new satellite instruments -> data management/data use

Exploitation of satellite data


• Proliferation of new satellite instruments -> data management/data use

• Massive investment in data handling (metadata, data management,efficient data dissemination) & monitoring (data evaluation) needed

• Important that a dialogue is maintained between the data suppliers(space agencies & NWP agencies) & end-users

See lectures on data assimilation (W. Bell, W.A. Lahoz)

•Operational use of research satellite data by significant numbers ofoperational centres: ozone (already assimilated operationally at ECMWF),stratospheric water vapour, CO2 and aerosols

•Assimilation of limb radiances by research & operational groups. Work ondeveloping fast & accurate forward models & interface between forward modeland assimilation. Progress more advanced for IR radiances than for UV/Visradiances (scattering effects for latter two)

The future


radiances (scattering effects for latter two)

•Chemical forecasting & air quality studies, including tropospheric pollutionforecasting & estimation of sources and sinks of pollutants & greenhouse gases

•Earth System approach to environmental & associated socio-economic issues.Incorporate biosphere & carbon cycle & coupling of all components of EarthSystem. GEMS project (Hollingsworth et al. 2008) & MACC

Andrews, D.G., 2000: An Introduction to Atmospheric Physics. Cambridge University Press.Charlton-Perez, A.J. et al., 2008: The frequency and dynamics of stratospheric suddenwarmings in the 2st century. J. Geophys. Res., 113, D16116, doi:10.1029/2007JD009571.Haltiner, G.J. & T.R. Williams, 1980: Numerical Prediction and Dynamic Meteorology. JohnWiley & Sons, New York.Harvey, V.L., et al., 2002: A climatology of stratospheric polar vortices and anticyclones. J.Geophys. Res., 107, NO. D20, 4442, doi:10.1029/2001JD001471.Harvey, V.L., et al., 2004: On the distribution of ozone in stratospheric anticyclones. J.Geophys. Res., 109, D24308, doi:10.1029/2004JD004992.Hollingsworth, A., et al., 2008. Toward a monitoring and forecasting system for atmosphericcomposition: The GEMS project. Bull. Amer. Meteorol. Soc., DOI:10.1175/2008BAMS2355.1.Lahoz, W.A., 2009: Research satellites. Data Assimilation: Making sense of observations, Eds.

Bibliography:


Lahoz, W.A., 2009: Research satellites. Data Assimilation: Making sense of observations, Eds.W.A. Lahoz, B. Khattatov and R. Ménard, Springer, due December 2009.Lahoz W.A., et al., 1994: Three-dimensional evolution of water vapor distributions in theNorthern Hemisphere stratosphere as observed by the MLS. J. Atmos. Sci., 51, 2914–2930.Lahoz W.A., et al., 1996: Vortex dynamics and the evolution of water vapour in thestratosphere of the Southern Hemisphere. Q. J. R. Meteorol. Soc., 122, 423–450.Lahoz, W.A., et al., 2009: Mesosphere-stratosphere transport during Southern Hemisphereautumn deduced from MIPAS observations. Q. J. R. Meteorol. Soc., 135, 681-694.Lahoz W.A., et al., 2006: Dynamical evolution of the 2003 Southern Hemisphere stratosphericwinter using Envisat trace gas observations. Q. J. R. Meteorol. Soc., 132, 1985–2008.Lahoz W.A., Geer A.J. & Orsolini Y.J., 2007: Northern Hemisphere stratospheric summerfrom MIPAS observations. Q. J. R. Meteorol. Soc., 133, 197–211.

Manney, G.L., et al., 1994: Stratospheric warmings during February and March 1993, Geophys.Res. Lett., 21, 813-816.Manney, G.L., et al., 1995: Formation of low-ozone pockets in the middle stratosphericanticyclone during winter. J. Geophys. Res., 100, 13,939-13,950.Manney, G.L., et al., 1999: Polar vortex dynamics during spring and fall diagnosed using tracegas observations from the Atmospheric Trace Molecule Spectroscopy instrument. J. Geophys.Res., 104, 18,841-18,866.Manney G.L., et al., 2005: EOS Microwave Limb Sounder observations of the Antarctic polarvortex breakup in 2004. Geophys. Res. Lett., 32, L12811, DOI:10.1029/2005GL022823.Manney, G.L., et al., 2006: EOS Microwave Limb Sounder observations of ‘‘frozen-in’’anticyclonic air in Arctic summer. Geophys. Res. Lett., 33, L06810,doi:10.1029/2005GL025418.Manney, G.L., et al., 2008: The evolution of the stratopause during the 2006 major warming:Satellite data and assimilated meteorological analyses. J. Geophys. Res., 113, D11115,doi:10.1029/2007JD009097.


doi:10.1029/2007JD009097.Manney, G.L., et al., 2008: The high Arctic in extreme winters: Vortex, temperature, and MLSand ACE-FTS trace gas evolution, Atmos. Chem. Phys., 8, 505– 522.Manney, G.L., et al., 2009: Aura Microwave Limb Sounder observations of dynamics andtransport during the record-breaking 2009 Arctic stratospheric major warming, Geophys.Res. Lett., 36, L12815, doi:10.1029/2009GL038586.Mote, P.W., et al., 1996. An atmospheric tape recorder: The imprint of tropical tropopausetemperatures on stratospheric water vapor, J. Geophys. Res., 101, 3989– 4006.NATO ASI, 2003: Data Assimilation for the Earth System, Eds. R. Swinbank, V. Shutyaev andW.A. Lahoz. Kluwer.Schoeberl, M.R., et al., 2006. The carbon monoxide tape recorder. Geophys. Res. Lett., 33,L12811, doi:10.1029/2006GL026178.

http://www.ecmwf.int/newsevents/training/lecture_notes/LN_DA.html

(Lecture notes on ECMWF course, including satellite data)

http://darc.nerc.ac.uk/asset

(ASSimilation of Envisat daTa, web-site. ASSET a EU FP5 project)

http://www.esa.int/esaEO/index.html

Web-sites:


http://www.esa.int/esaEO/index.html

(ESA web-site with Envisat and other images)

http://www.nasa.gov

(NASA web-site with many satellite images)

http://www.temis.nl

(KNMI Website for ozone data, analyses & forecasts)

Extra slides


• Depending on wavelength, radiation at top of

• Atmosphere is sensitive to different atmospheric constituents (Courtesy ECMWF)

Scat, Altimeter

AMSU, SSM/I

HIRS GOES METEOSAT AIRS

SBUV

Sampling EM spectrum using satellites


Is the Earth’s ozone layer recovering?

Is air quality getting worse?

How is Earth’s climate changing?

Questions that Eos Aura & Envisat could help answer

Envisat: http://envisat.esa.int


X


UARS orbits: Oct 1991 – Dec 1993. From UARS web-site


CO tape recorder, Schoeberl et al. 2006, GRL


1. Sondes have good vertical resolution, but limited height range

2. Retrievals have a poorer resolution, greater height range and better coverage.

-> We cannot compare them directly

Intercomparison MIPAS retrieval/ozonesondes

To compare a simulated retrieval based on the sonde profile with MIPAS

retrieval: convolve sonde profile with MIPAS averaging kernel

Ozone Intercomparison

40

60

80H

eig

ht

[km

]

10.00

1.00

0.10

0.01

Pre

ssur

e [h

Pa]

80 km

40 km

10 mb

0.1 mb

-0.5 0.0 0.5 1.0MIPAS ozone averaging kernels

0

20

He

igh

t [k

m]

0 2 4 6 8 10Ozone VMR [ppmv]

1000.00

100.00

10.00

Pre

ssur

e [h

Pa]

Averaging Kernels–– sonde

* retrieval

◊ simulation

0 km 1000 mb

10 mb

10 ppmvMIPAS

Courtesy Clive Rodgers

3-d filamentation: spatial structure

CH4 cross-section “1” & PV (potential vorticity)

CH4 PV

Schematic for NH stratospheric winter (note artwork!):

Tracer isopleth

e.g. H2O

Polar vortexAleutian High

Schematic for SH stratospheric late winter/spring:

”Champagne glass”

Tracer isopleth

e.g. H2O

Polar vortexQuasi-stationary

anticyclone

observations, lecture 2 research satellite observations of

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